Hellinger distance-based stable sparse feature selection for high-dimensional class-imbalanced data.

ABSTRACT: BACKGROUND:Feature selection in class-imbalance learning has gained increasing attention in recent years due to the massive growth of high-dimensional class-imbalanced data across many scientific fields. In addition to reducing model complexity and discovering key biomarkers, feature selection is also an effective method of combating overlapping which may arise in such data and become a crucial aspect for determining classification performance. However, ordinary feature selection techniques for classification can not be simply used for addressing class-imbalanced data without any adjustment. Thus, more efficient feature selection technique must be developed for complicated class-imbalanced data, especially in the context of high-dimensionality. RESULTS:We proposed an algorithm called sssHD

SUBMITTER: Fu GH 

PROVIDER: S-EPMC7092448 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature